adversarial bandit

Near-Optimal Regret for Distributed Adversarial Bandits: A Black-Box Approach COLT 2026 Faster Rates for Private Adversarial Bandits ICML 2025 uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs ICLR 2025 Bandits with Abstention under Expert Advice NeurIPS 2024 Beyond Worst-case Attacks: Robust RL with Adaptive Defense via Non-dominated Policies ICLR 2024 Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds COLT 2024 PRODuctive bandits: Importance Weighting No More NeurIPS 2024 Slowly Changing Adversarial Bandit Algorithms are Efficient for Discounted MDPs ALT 2024 Taming Heavy-Tailed Losses in Adversarial Bandits and the Best-of-Both-Worlds Setting NeurIPS 2024 An Improved Relaxation for Oracle-Efficient Adversarial Contextual Bandits NeurIPS 2023 Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning ICML 2023 Bayesian Design Principles for Frequentist Sequential Learning ICML 2023 Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs ALT 2023 Meta-Learning Adversarial Bandit Algorithms NeurIPS 2023 Online Learning with Off-Policy Feedback ALT 2023 Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds NeurIPS 2023 Bounded Memory Adversarial Bandits with Composite Anonymous Delayed Feedback IJCAI 2022 Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits COLT 2022 Diversified Recommendations for Agents with Adaptive Preferences NeurIPS 2022 Greedy when Sure and Conservative when Uncertain about the Opponents ICML 2022 Hierarchical Graph Transformer with Adaptive Node Sampling NeurIPS 2022 Nested Bandits ICML 2022 No Weighted-Regret Learning in Adversarial Bandits with Delays JMLR 2022 Rejection sampling from shape-constrained distributions in sublinear time AISTATS 2022 Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback ICML 2022